Class/Object

com.microsoft.ml.spark

TrainClassifier

Related Docs: object TrainClassifier | package spark

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class TrainClassifier extends Estimator[TrainedClassifierModel] with HasLabelCol with ComplexParamsWritable with HasFeaturesCol

Trains a classification model. Featurizes the given data into a vector of doubles.

Note the behavior of the reindex and labels parameters, the parameters interact as:

reindex -> false labels -> false (Empty) Assume all double values, don't use metadata, assume natural ordering

reindex -> true labels -> false (Empty) Index, use natural ordering of string indexer

reindex -> false labels -> true (Specified) Assume user knows indexing, apply label values. Currently only string type supported.

reindex -> true labels -> true (Specified) Validate labels matches column type, try to recast to label type, reindex label column

Linear Supertypes
HasFeaturesCol, ComplexParamsWritable, MLWritable, HasLabelCol, Wrappable, Estimator[TrainedClassifierModel], PipelineStage, org.apache.spark.internal.Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. TrainClassifier
  2. HasFeaturesCol
  3. ComplexParamsWritable
  4. MLWritable
  5. HasLabelCol
  6. Wrappable
  7. Estimator
  8. PipelineStage
  9. Logging
  10. Params
  11. Serializable
  12. Serializable
  13. Identifiable
  14. AnyRef
  15. Any
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Visibility
  1. Public
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Instance Constructors

  1. new TrainClassifier()

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  2. new TrainClassifier(uid: String)

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params

  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  5. def BooleanParam(i: Identifiable, name: String, description: String, default: Boolean): BooleanParam

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    Definition Classes
    Wrappable

  6. def BooleanParam(i: Identifiable, name: String, description: String): BooleanParam

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    Definition Classes
    Wrappable

  7. def DoubleParam(i: Identifiable, name: String, description: String, default: Double): DoubleParam

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    Definition Classes
    Wrappable

  8. def DoubleParam(i: Identifiable, name: String, description: String): DoubleParam

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    Definition Classes
    Wrappable

  9. def IntParam(i: Identifiable, name: String, description: String, validation: (Int) ⇒ Boolean): IntParam

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    Definition Classes
    Wrappable

  10. def IntParam(i: Identifiable, name: String, description: String, default: Int): IntParam

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    Definition Classes
    Wrappable

  11. def IntParam(i: Identifiable, name: String, description: String): IntParam

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    Definition Classes
    Wrappable

  12. def LongParam(i: Identifiable, name: String, description: String, default: Long): LongParam

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    Definition Classes
    Wrappable

  13. def LongParam(i: Identifiable, name: String, description: String): LongParam

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    Definition Classes
    Wrappable

  14. def StringParam(i: Identifiable, name: String, description: String, default: String, domain: Seq[String]): Param[String]

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    Definition Classes
    Wrappable

  15. def StringParam(i: Identifiable, name: String, description: String, default: String): Param[String]

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    Definition Classes
    Wrappable

  16. def StringParam(i: Identifiable, name: String, description: String, validation: (String) ⇒ Boolean): Param[String]

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    Definition Classes
    Wrappable

  17. def StringParam(i: Identifiable, name: String, description: String): Param[String]

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    Definition Classes
    Wrappable

  18. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  19. def chainedUid(origin: String): String

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    Definition Classes
    Wrappable

  20. final def clear(param: Param[_]): TrainClassifier.this.type

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    Definition Classes
    Params

  21. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  22. def convertLabel(dataset: Dataset[_], labelColumn: String, labelValues: Option[Array[_]]): (DataFrame, Option[Array[_]])

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  23. def copy(extra: ParamMap): Estimator[TrainedClassifierModel]

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    Definition Classes
    TrainClassifier → Estimator → PipelineStage → Params

  24. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
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    Params

  25. final def defaultCopy[T <: Params](extra: ParamMap): T

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    protected
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    Params

  26. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  27. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  28. def explainParam(param: Param[_]): String

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    Definition Classes
    Params

  29. def explainParams(): String

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    Definition Classes
    Params

  30. final def extractParamMap(): ParamMap

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    Definition Classes
    Params

  31. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params

  32. val featuresCol: Param[String]

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    The name of the features column

    The name of the features column

    Definition Classes
    HasFeaturesCol

  33. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  34. def fit(dataset: Dataset[_]): TrainedClassifierModel

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    Fits the classification model.

    Fits the classification model.

    dataset

    The input dataset to train.

    returns

    The trained classification model.

    Definition Classes
    TrainClassifier → Estimator

  35. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[TrainedClassifierModel]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )

  36. def fit(dataset: Dataset[_], paramMap: ParamMap): TrainedClassifierModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )

  37. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TrainedClassifierModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()

  38. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  39. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  40. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  41. def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol

  42. def getFeaturizeParams: (Boolean, Boolean, Int)

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  43. def getLabelCol: String

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    Definition Classes
    HasLabelCol

  44. def getLabels: Array[String]

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  45. def getModel: Estimator[_ <: Model[_]]

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  46. def getNumFeatures: Int

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  47. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params

  48. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params

  49. def getReindexLabel: Boolean

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  50. final def hasDefault[T](param: Param[T]): Boolean

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  51. def hasParam(paramName: String): Boolean

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    Params

  52. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  53. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging

  54. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Logging

  55. final def isDefined(param: Param[_]): Boolean

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  56. final def isInstanceOf[T0]: Boolean

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    Any

  57. final def isSet(param: Param[_]): Boolean

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  58. def isTraceEnabled(): Boolean

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    Attributes
    protected
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    Logging

  59. val labelCol: Param[String]

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    The name of the label column

    The name of the label column

    Definition Classes
    HasLabelCol

  60. val labels: StringArrayParam

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    Specifies the labels metadata on the column.

    Specifies the labels metadata on the column. See class documentation for how this parameter interacts with reindex labels parameter.

  61. def log: Logger

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    Logging

  62. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Logging

  63. def logDebug(msg: ⇒ String): Unit

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    Logging

  64. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Logging

  65. def logError(msg: ⇒ String): Unit

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    Logging

  66. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Logging

  67. def logInfo(msg: ⇒ String): Unit

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    Logging

  68. def logName: String

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    Logging

  69. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Logging

  70. def logTrace(msg: ⇒ String): Unit

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    Logging

  71. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Logging

  72. def logWarning(msg: ⇒ String): Unit

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    Logging

  73. val model: EstimatorParam

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    Classifier to run.

    Classifier to run. The currently supported classifiers are: Logistic Regression Classifier Decision Tree Classifier Random Forest Classifier Gradient Boosted Trees Classifier Naive Bayes Classifier Multilayer Perceptron Classifier In addition to any generic learner that inherits from Predictor.

  74. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  75. final def notify(): Unit

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    Definition Classes
    AnyRef

  76. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  77. val numFeatures: IntParam

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    Number of features to hash to

  78. val paramDomains: Map[String, Seq[String]]

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    Definition Classes
    Wrappable

  79. lazy val params: Array[Param[_]]

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    Definition Classes
    Params

  80. val reindexLabel: BooleanParam

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    Specifies whether to reindex the given label column.

    Specifies whether to reindex the given label column. See class documentation for how this parameter interacts with specified labels.

  81. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )

  82. final def set(paramPair: ParamPair[_]): TrainClassifier.this.type

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    Params

  83. final def set(param: String, value: Any): TrainClassifier.this.type

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  84. final def set[T](param: Param[T], value: T): TrainClassifier.this.type

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    Definition Classes
    Params

  85. final def setDefault(paramPairs: ParamPair[_]*): TrainClassifier.this.type

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    Attributes
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  86. final def setDefault[T](param: Param[T], value: T): TrainClassifier.this.type

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    Attributes
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    Params

  87. def setFeaturesCol(value: String): TrainClassifier.this.type

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    Definition Classes
    HasFeaturesCol

  88. def setLabelCol(value: String): TrainClassifier.this.type

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    Definition Classes
    HasLabelCol

  89. def setLabels(value: Array[String]): TrainClassifier.this.type

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  90. def setModel(value: Estimator[_ <: Model[_]]): TrainClassifier.this.type

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  91. def setNumFeatures(value: Int): TrainClassifier.this.type

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  92. def setReindexLabel(value: Boolean): TrainClassifier.this.type

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  93. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef

  94. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any

  95. def transformSchema(schema: StructType): StructType

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    Definition Classes
    TrainClassifier → PipelineStage
    Annotations
    @DeveloperApi()

  96. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
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    PipelineStage
    Annotations
    @DeveloperApi()

  97. val uid: String

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    Definition Classes
    TrainClassifier → Identifiable

  98. final def wait(): Unit

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    @throws( ... )

  99. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )

  100. final def wait(arg0: Long): Unit

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    @throws( ... )

  101. def write: MLWriter

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    Definition Classes
    ComplexParamsWritable → MLWritable

Inherited from HasFeaturesCol

Inherited from ComplexParamsWritable

Inherited from MLWritable

Inherited from HasLabelCol

Inherited from Wrappable

Inherited from Estimator[TrainedClassifierModel]

Inherited from PipelineStage

Inherited from org.apache.spark.internal.Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters

Parameter setters

Parameter getters

Members